There has been a huge increase in demand for comprehensive quantitative analysis of neurovascular imaging data produced in the clinical setting for diseases such as multiple sclerosis, traumatic brain injury, stroke and dementia. Our objective in this project is to design and develop advanced image processing software that can rapidly and accurately analyze such data. To achieve this objective, we propose a range of novel algorithms to process data from the following MR imaging sequences widely used in the aforementioned applications: time resolved 3D contrast enhanced MR angiography (CE-MRA) for the assessment of vascular anatomy, time resolved 2D phase contrast flow imaging (PC-MRI) for the evaluation of vascular hemodynamics, susceptibility weighted imaging (SWI) for quantifying iron deposition in the brain, and fluid attenuated inversion recovery (FLAIR) imaging for the detection of white matter hyperintensities (WMH) and lesions. A variety of tools will be designed and implemented to tackle these problems including: tissue similarity mapping and active shape models to segment the vasculature in both CE-MRA and PC-MRI images; automatic tissue segmentation in the basal ganglia and thalamus for a two-region of interest analysis for iron quantification with SWI; and finally adaptive approaches incorporating fuzzy C-means, shape factor analysis, compactness and fractional anisotropy to quantify lesions and WMHs. To exploit the advantages provided by different imaging sequences, co-registration algorithms will be used to improve segmentation of vessels between CE-MRA and PC-MRI, and between 3D T1 weighted imaging and SWI. Upon finishing this project, we expect a multi-fold increase in processing efficiency and a significant increase in accuracy will be achieved. The resulting software will not only help the growth of our company, but also improve the diagnosis and treatment of neurovascular diseases.